InversionNet3D: Efficient and Scalable Learning for 3-D Full-Waveform Inversion

Zeng, Qili; Feng, Shihang; Wohlberg, Brendt Egon; Lin, Youzuo

doi:10.1109/tgrs.2021.3135354

Title: InversionNet3D: Efficient and Scalable Learning for 3-D Full-Waveform Inversion

Journal Article · 13 December 2021 · IEEE Transactions on Geoscience and Remote Sensing

DOI: https://doi.org/10.1109/tgrs.2021.3135354 · OSTI ID:1900478

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Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Seismic full-waveform inversion (FWI) techniques aim to find a high-resolution subsurface geophysical model provided with waveform data. Some recent effort in data-driven FWI has shown some encouraging results in obtaining 2-D velocity maps. However, due to high computational complexity and large memory consumption, the reconstruction of 3-D high-resolution velocity maps via deep networks is still a great challenge. Here, in this article, we present InversionNet3D (InvNet3D), an efficient and scalable encoder–decoder network for 3-D FWI. The proposed method employs group convolution in the encoder to establish an effective hierarchy for learning information from multiple sources while cutting down unnecessary parameters and operations at the same time. The introduction of invertible layers further reduces the memory consumption of intermediate features during training and, thus, enables the development of deeper networks with more layers and higher capacity as required by different application scenarios. Experiments on the 3-D Kimberlina dataset demonstrate that InvNet3D achieves state-of-the-art reconstruction performance with lower computational cost and lower memory footprint compared to the baseline.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1900478

Report Number(s):: LA-UR-21-22293

Journal Information:: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 60; ISSN 0196-2892

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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